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existing data augmentation methods often overlook text relevance and may disrupt semantic structures and dependencies, making it difficult to generate effective augmented data for improving model generalization.\n  In this paper, we propose Structured Semantic Data Augmentation (SSDAU), a novel method designed to preserve the semantic structure of text during augmentation.\n  SSDAU segments text based on entity labels and employs an encoder to capture semantic features of entities through context awareness.\n  It then performs entity semantic restructuring to generate augmented data.\n  To distinguish semantically similar entities, SSDAU fuses contextualized embeddings with traditional similarity scores.\n  To m","title":"SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction","url":"https://arxiv.org/abs/2605.23440","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.23440v1 Announce Type: cross \nAbstract: Joint Entity and Relation Extraction (JERE) is highly 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